In the last decade, machine learning and deep learning algorithms have become widespread tools to address many computer vision-based problems, including medical imaging analysis [1]}, person re-identification [2]}, [3]}, environment monitoring [4]}, [5]}, [6]}, emotion recognition [7]}, handwriting validation [8]}, [9]}, background modeling [10]}, and video synthesis [11]}.
Although effective, these methods usually require a large amount of training data which, however, is not always available. To solve this issue, transfer learning approaches are being exploited to retain previous knowledge from different tasks, and reduce the amount of data required to address the new one [12]}, [13]}.
A paradigm that is proving particularly relevant to the medical field, where sensitive data is generally employed. As a matter of fact, automatic procedures for detection, classification, and analysis are already being developed [14]}, [15]}. What is more, in concert with these diagnosis-related procedures, mobile applications are also being explored to support patients in their daily routine [16]}, [17]}, [18]} since, in general, dealing with the medical aspects of life is a challenging task for both medical operators and sick persons. In particular, operators have to manage several, often elder, people with different pathologies throughout the day. To help this category, applications noticing anomalous patterns can provide a huge healthcare boost, as shown in [19]}, where bed falls are detected even at nighttime to safeguard patients. Regarding the latter, instead, they tend to either forget when to take a medicine or its required dosage, as reported by [20]}. Although it is possible to look up these instructions on the internet or the patient information leaflet (PIL), it can be difficult to find the correct information; suggesting that there is a technological gap that can be filled.
